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Data Acquisition Filtering Focused on Optimizing Transmission in a LoRaWAN Network Applied to the WSN Forest Monitoring System

Developing innovative systems and operations to monitor forests and send alerts in dangerous situations, such as fires, has become, over the years, a necessary task to protect forests. In this work, a Wireless Sensor Network (WSN) is employed for forest data acquisition to identify abrupt anomalies...

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Autores principales: Brito, Thadeu, Azevedo, Beatriz Flamia, Mendes, João, Zorawski, Matheus, Fernandes, Florbela P., Pereira, Ana I., Rufino, José, Lima, José, Costa, Paulo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9921254/
https://www.ncbi.nlm.nih.gov/pubmed/36772322
http://dx.doi.org/10.3390/s23031282
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author Brito, Thadeu
Azevedo, Beatriz Flamia
Mendes, João
Zorawski, Matheus
Fernandes, Florbela P.
Pereira, Ana I.
Rufino, José
Lima, José
Costa, Paulo
author_facet Brito, Thadeu
Azevedo, Beatriz Flamia
Mendes, João
Zorawski, Matheus
Fernandes, Florbela P.
Pereira, Ana I.
Rufino, José
Lima, José
Costa, Paulo
author_sort Brito, Thadeu
collection PubMed
description Developing innovative systems and operations to monitor forests and send alerts in dangerous situations, such as fires, has become, over the years, a necessary task to protect forests. In this work, a Wireless Sensor Network (WSN) is employed for forest data acquisition to identify abrupt anomalies when a fire ignition starts. Even though a low-power LoRaWAN network is used, each module still needs to save power as much as possible to avoid periodic maintenance since a current consumption peak happens while sending messages. Moreover, considering the LoRaWAN characteristics, each module should use the bandwidth only when essential. Therefore, four algorithms were tested and calibrated along real and monitored events of a wildfire. The first algorithm is based on the Exponential Smoothing method, Moving Averages techniques are used to define the other two algorithms, and the fourth uses the Least Mean Square. When properly combined, the algorithms can perform a pre-filtering data acquisition before each module uses the LoRaWAN network and, consequently, save energy if there is no necessity to send data. After the validations, using Wildfire Simulation Events (WSE), the developed filter achieves an accuracy rate of 0.73 with 0.5 possible false alerts. These rates do not represent a final warning to firefighters, and a possible improvement can be achieved through cloud-based server algorithms. By comparing the current consumption before and after the proposed implementation, the modules can save almost 53% of their batteries when is no demand to send data. At the same time, the modules can maintain the server informed with a minimum interval of 15 min and recognize abrupt changes in 60 s when fire ignition appears.
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spelling pubmed-99212542023-02-12 Data Acquisition Filtering Focused on Optimizing Transmission in a LoRaWAN Network Applied to the WSN Forest Monitoring System Brito, Thadeu Azevedo, Beatriz Flamia Mendes, João Zorawski, Matheus Fernandes, Florbela P. Pereira, Ana I. Rufino, José Lima, José Costa, Paulo Sensors (Basel) Article Developing innovative systems and operations to monitor forests and send alerts in dangerous situations, such as fires, has become, over the years, a necessary task to protect forests. In this work, a Wireless Sensor Network (WSN) is employed for forest data acquisition to identify abrupt anomalies when a fire ignition starts. Even though a low-power LoRaWAN network is used, each module still needs to save power as much as possible to avoid periodic maintenance since a current consumption peak happens while sending messages. Moreover, considering the LoRaWAN characteristics, each module should use the bandwidth only when essential. Therefore, four algorithms were tested and calibrated along real and monitored events of a wildfire. The first algorithm is based on the Exponential Smoothing method, Moving Averages techniques are used to define the other two algorithms, and the fourth uses the Least Mean Square. When properly combined, the algorithms can perform a pre-filtering data acquisition before each module uses the LoRaWAN network and, consequently, save energy if there is no necessity to send data. After the validations, using Wildfire Simulation Events (WSE), the developed filter achieves an accuracy rate of 0.73 with 0.5 possible false alerts. These rates do not represent a final warning to firefighters, and a possible improvement can be achieved through cloud-based server algorithms. By comparing the current consumption before and after the proposed implementation, the modules can save almost 53% of their batteries when is no demand to send data. At the same time, the modules can maintain the server informed with a minimum interval of 15 min and recognize abrupt changes in 60 s when fire ignition appears. MDPI 2023-01-22 /pmc/articles/PMC9921254/ /pubmed/36772322 http://dx.doi.org/10.3390/s23031282 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Brito, Thadeu
Azevedo, Beatriz Flamia
Mendes, João
Zorawski, Matheus
Fernandes, Florbela P.
Pereira, Ana I.
Rufino, José
Lima, José
Costa, Paulo
Data Acquisition Filtering Focused on Optimizing Transmission in a LoRaWAN Network Applied to the WSN Forest Monitoring System
title Data Acquisition Filtering Focused on Optimizing Transmission in a LoRaWAN Network Applied to the WSN Forest Monitoring System
title_full Data Acquisition Filtering Focused on Optimizing Transmission in a LoRaWAN Network Applied to the WSN Forest Monitoring System
title_fullStr Data Acquisition Filtering Focused on Optimizing Transmission in a LoRaWAN Network Applied to the WSN Forest Monitoring System
title_full_unstemmed Data Acquisition Filtering Focused on Optimizing Transmission in a LoRaWAN Network Applied to the WSN Forest Monitoring System
title_short Data Acquisition Filtering Focused on Optimizing Transmission in a LoRaWAN Network Applied to the WSN Forest Monitoring System
title_sort data acquisition filtering focused on optimizing transmission in a lorawan network applied to the wsn forest monitoring system
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9921254/
https://www.ncbi.nlm.nih.gov/pubmed/36772322
http://dx.doi.org/10.3390/s23031282
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